Artificial intelligence has steadily entered the Governance, Risk, and Compliance landscape, yet much of its deployment has remained limited to task automation. From drafting policies to extracting clauses from contracts and regulatory documents, most AI tools in GRC have focused on assisting professionals rather than transforming the structure of compliance operations. Industry observers note that while these capabilities improve efficiency, they still rely heavily on manual oversight and traditional workflows that demand constant human coordination.
According to Yair Kuznitsov, CEO of Anecdotes, the next phase of AI adoption in GRC moves beyond assisting with isolated tasks and instead replaces the workflow itself. He describes this shift as agentic GRC, where autonomous agents do not merely support compliance teams but actively execute and manage core compliance functions. In this model, AI agents continuously collect evidence across systems, evaluate the effectiveness of controls, trigger remediation processes when gaps are identified, and maintain detailed audit trails without waiting for manual prompts. Rather than functioning as digital assistants that respond to requests, these agents operate within the compliance environment with embedded decision making capabilities.
This approach fundamentally changes how organizations manage risk and regulatory obligations. Traditional GRC processes often require teams to gather documentation from various departments, validate controls against evolving standards, and prepare audit materials in cycles that can be time consuming and reactive. Agentic systems, as described by Kuznitsov, embed decision making directly into the operational layer. Controls are not simply documented; they are continuously assessed. Evidence is not gathered only during audit season; it is captured in real time. When an issue arises, the system can initiate corrective actions based on predefined policies and risk thresholds. This creates a dynamic compliance environment where oversight is continuous rather than periodic.
The implications extend to transparency and accountability as well. Maintaining audit trails has long been a labor intensive exercise, often dependent on manual documentation and fragmented data sources. In an agentic GRC framework, every action taken by the system is logged and traceable, offering regulators and auditors a clearer line of sight into how decisions were made and how risks were addressed. By embedding intelligence within the workflow, organizations can shift from reactive compliance to proactive governance. Kuznitsov emphasizes that this model does not eliminate human oversight but redefines it. Compliance professionals transition from performing repetitive coordination tasks to supervising intelligent systems, interpreting insights, and refining risk strategies. As enterprises navigate expanding regulatory requirements and increasing operational complexity, the move toward autonomous, decision enabled GRC systems signals a broader evolution in how technology supports corporate governance.
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